Suboptimal iron deficiency screening in pregnancy and the impact of socioeconomic status in a high-resource setting
Bibliographic record
Abstract
Iron deficiency (ID) anemia in pregnancy is associated with poor maternal and childhood outcomes, yet ferritin testing, the standard test for ID, is not considered part of routine prenatal bloodwork in Canada. We conducted a retrospective cohort study of 44 552 pregnant patients with prenatal testing at community laboratories in Ontario, Canada, to determine the prevalence of ferritin testing over 5 years. Secondary objectives were to determine the prevalence and severity of ID and to identify clinical and demographic variables that influence the likelihood of ID screening. A total of 59.4% of patients had a ferritin checked during pregnancy; 71.4% were ordered in the first trimester, when the risk of ID is lowest. Excluding patients with abnormally elevated ferritins, 25.2% were iron insufficient (30-44 µg/L) and 52.8% were iron deficient (≤29 µg/L) at least once in pregnancy. A total of 8.3% were anemic (hemoglobin <105 g/L). The proportion of anemic patients with a subsequent ferritin test in pregnancy ranged from 22% to 67% in the lowest and highest anemia severity categories, respectively. Lower annual household income was negatively associated with the odds of a ferritin test; compared with those in the fifth (ie, highest) income quintile, the odds of ferritin testing for patients in the first, second, and fourth quintiles were 0.83 (95% confidence interval [CI], 0.74-0.91), 0.82 (95% CI, 0.74-0.91), and 0.86 (95% CI, 0.77-0.97), respectively. These data highlight gaps in prenatal care and issues of health equity that warrant harmonization of obstetrical guidelines to recommend routine ferritin testing in pregnancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".